In radiation therapy, accurately identifying and delineating anatomic structures during the treatment planning phase is critically important. The objective of every such procedure is to provide an accurate definition of a target volume and any organs at risk in order to deliver the maximum radiation dose to the target volume (e.g., tumor) while sparing the surrounding healthy tissue from being subject to exposure to potentially harmful radiation.
During the planning stage, a planner often defines specific structures used to control the dose distribution during treatment optimizations. Some of these structures are Boolean or other combinations of targets and/or normal tissues. By defining the intersection of targets and normal tissues as separate structures, different prescription doses and constraints can easily be applied to different regions, facilitating the creation of controlled dose gradients between normal tissues and targets.
To identify and delineate these structures the planner (typically a specialist such as a technician, clinician, or oncologist) reviews one or more images of a target volume in a therapy subject and draws contours within the images that delineate specific volumes and organs at risk. Due to both the importance and the complexities of the process, the delineation and delineation quality check process can take many hours of a planner's time if performed manually. Automatic structure delineation tools have been developed to alleviate some of the burden on the treatment planners. One type of automatic structure delineation is the derivation of structures which is performed by constructing structures based on pre-defined structures, and typically includes defining a sequence of operations that creates an output structure from a set of input structures.
Unfortunately, not all structures can be accurately segmented automatically by the tools that are currently available. Moreover, current treatment planning solutions provide only generic tools for structure derivation that are not optimized for radiation therapy, require mathematical expertise to use properly, and/or are difficult for many radiation oncologists to intuitively understand. Each of these drawbacks increases the likelihood of human error and the potential for harmful radiation to be misdirected. Many conventional tools rely on the creation and usage of Boolean operations (which can be difficult to understand), and on the generation of temporary structures to store intermediate results that are of little to no clinical interest. Some approaches provide only partial functionalities requiring users to switch between tools. Moreover, the same solutions often require significant manual execution and re-application when the input structure(s) are updated, or the underlying data of the input structure(s) change, all of which further presents opportunities to introduce errors or unintended/undesirable outputs.